New echocardiographic imaging method based on the bandwidth of the ultrasound Doppler signal with applications in blood/tissue segmentation in the left ventricle

Spatiotemporal singular value decomposition for denoising in photoacoustic imaging with a low-energy excitation light source

Photoacoustic (PA) imaging is an emerging hybrid imaging modality that combines rich optical spectroscopic contrast and high ultrasonic resolution, and thus holds tremendous promise for a wide range of pre-clinical and clinical applications. Compact and affordable light sources such as light-emitting diodes (LEDs) and laser diodes (LDs) are promising alternatives to bulky and expensive solid-state laser systems that are commonly used as PA light sources. These could accelerate the clinical translation of PA technology. However, PA signals generated with these light sources are readily degraded by noise due to the low optical fluence, leading to decreased signal-to-noise ratio (SNR) in PA images. In this work, a spatiotemporal singular value decomposition (SVD) based PA denoising method was investigated for these light sources that usually have low fluence and high repetition rates. The proposed method leverages both spatial and temporal correlations between radiofrequency (RF) data frames. Validation was performed on simulations and in vivo PA data acquired from human fingers (2D) and forearm (3D) using a LED-based system. Spatiotemporal SVD greatly enhanced the PA signals of blood vessels corrupted by noise while preserving a high temporal resolution to slow motions, improving the SNR of in vivo PA images by 90.3%, 56.0%, and 187.4% compared to single frame-based wavelet denoising, averaging across 200 frames, and single frame without denoising, respectively. With a fast processing time of SVD (∼50 µs per frame), the proposed method is well suited to PA imaging systems with low-energy excitation light sources for real-time in vivo applications.

Adaptive Spatiotemporal SVD Clutter Filtering for Ultrafast Doppler Imaging Using Similarity of Spatial Singular Vectors

Singular value decomposition of ultrafast imaging ultrasonic data sets has recently been shown to build a vector basis far more adapted to the discrimination of tissue and blood flow than the classical Fourier basis, improving by large factor clutter filtering and blood flow estimation. However, the question of optimally estimating the boundary between the tissue subspace and the blood flow subspace remained unanswered. Here, we introduce an efficient estimator for automatic thresholding of subspaces and compare it to an exhaustive list of thirteen estimators that could achieve this task based on the main characteristics of the singular components, namely the singular values, the temporal singular vectors, and the spatial singular vectors. The performance of those fourteen estimators was tested in vitro in a large set of controlled experimental conditions with different tissue motion and flow speeds on a phantom. The estimator based on the degree of resemblance of spatial singular vectors outperformed all others. Apart from solving the thresholding problem, the additional benefit with this estimator was its denoising capabilities, strongly increasing the contrast to noise ratio and lowering the noise floor by at least 5 dB. This confirms that, contrary to conventional clutter filtering techniques that are almost exclusively based on temporal characteristics, efficient clutter filtering of ultrafast Doppler imaging cannot overlook space. Finally, this estimator was applied in vivo on various organs (human brain, kidney, carotid, and thyroid) and showed efficient clutter filtering and noise suppression, improving largely the dynamic range of the obtained ultrafast power Doppler images.

Late gadolinium enhancement in cardiac MRI in patients with severe aortic stenosis and preserved left ventricular systolic function is related to attenuated improvement of left ventricular geometry and filling pressure after aortic valve replacement

Background and Objectives

We investigated echocardiographic predictors: left ventricular (LV) geometric changes following aortic valve replacement (AVR) according to the late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) in patients with severe aortic stenosis (AS) and preserved LV systolic function.

Subjects and Methods

We analyzed 41 patients (24 males, 63.1±8.7 years) with preserved LV systolic function who were scheduled to undergo AVR for severe AS. All patients were examined with transthoracic echocardiography (TTE), CMR before and after AVR (in the hospital) and serial TTEs (at 6 and 12 months) were repeated.

Results

The group with LGE (LGE+) showed greater wall thickness (septum, 14.3±2.6 mm vs. 11.5±2.0 mm, p=0.001, posterior; 14.3±2.5 mm vs. 11.4±1.6 mm, p<0.001), lower tissue Doppler image (TDIS', 4.4±1.4 cm/s vs. 5.5±1.2 cm/s, p=0.021; TDI E', 3.2±0.9 cm/s vs. 4.8±1.4 cm/s, p=0.002), and greater E/e' (21.8±10.3 vs. 15.4±6.3, p=0.066) than those without LGE (LGE-). Multivariate analysis show that TDI e' (odds ratio=0.078, 95% confidence interval=0.007-0.888, p=0.040) was an independent determinant of LGE+. In an analysis of the 6- and 12-month follow-up compared with pre-AVR, LGE- showed decreased LV end-diastolic diameter (48.3±5.0 mm vs. 45.8±3.6 mm, p=0.027; 48.3±5.0 mm vs. 46.5±3.4 mm, p=0.019). Moreover, E/e' (at 12 months) showed further improved LV filling pressure (16.0±6.6 vs. 12.3±4.3, p=0.001) compared with pre-AVR. However, LGE+ showed no significant improvement.

Conclusion

The absence of LGE is associated with favorable improvements in LV geometry and filling pressure. TDI E' is an independent determinant of LGE in patients with severe AS and preserved LV systolic function.

Techniques to derive geometries for image-based Eulerian computations

PURPOSE

The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted.

DESIGN/METHODOLOGY/APPROACH

Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures.

FINDINGS

While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics.

ORIGINALITY/VALUE

It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.

New Doppler-based imaging method in echocardiography with applications in blood/tissue segmentation

A parametric model for the ultrasound signals from blood and tissue is developed and a new imaging method, knowledge-based imaging, is defined. This method utilizes the likelihood ratio function to classify blood and tissue signals. The method separates blood and tissue signals by the difference in movement patterns in addition to the difference in powers. The prior information about the levels of expected system white noise and clutter noise are utilized to enhance the image quality. The implementation of knowledge-based imaging is outlined, and some knowledge-based images with different parameter settings are visually compared with a second-harmonic image, a fundamental image and a bandwidth image. In order to understand the parameter estimation process a computer simulation is introduced to outline the differences between the imaging methods. The apparent error rates are calculated in any reasonable tissue to blood signal ratio, tissue to white noise ratio and clutter to white noise ratio. A discussion of further development of knowledge-based imaging is also described in this paper.